Learning Combinations of Activation Functions

In the last decade, an active area of research has been devoted to design novel activation functions that are able to help deep neural networks to converge, obtaining better performance. The training procedure of these architectures usually involves optimization of the weights of their layers only, while non-linearities are generally pre-specified and their (possible) parameters are usually considered as hyper-parameters to be tuned manually. In this paper, we introduce two approaches to automatically learn different combinations of base activation functions (such as the identity function, ReLU, and tanh) during the training phase. We present a thorough comparison of our novel approaches with well-known architectures (such as LeNet-5, AlexNet, and ResNet-56) on three standard datasets (Fashion-MNIST, CIFAR-10, and ILSVRC-2012), showing substantial improvements in the overall performance, such as an increase in the top-1 accuracy for AlexNet on ILSVRC-2012 of 3.01 percentage points.Comment: 6 pages, 3 figures. Published as a conference paper at ICPR 2018. Code: https://bitbucket.org/francux/learning_combinations_of_activation_function

Automated Pruning for Deep Neural Network Compression

In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks. The novelty of the proposed pruning technique is in its differentiability, which allows pruning to be performed during the backpropagation phase of the network training. This enables an end-to-end learning and strongly reduces the training time. The technique is based on a family of differentiable pruning functions and a new regularizer specifically designed to enforce pruning. The experimental results show that the joint optimization of both the thresholds and the network weights permits to reach a higher compression rate, reducing the number of weights of the pruned network by a further 14% to 33% compared to the current state-of-the-art. Furthermore, we believe that this is the first study where the generalization capabilities in transfer learning tasks of the features extracted by a pruned network are analyzed. To achieve this goal, we show that the representations learned using the proposed pruning methodology maintain the same effectiveness and generality of those learned by the corresponding non-compressed network on a set of different recognition tasks.Comment: 8 pages, 5 figures. Published as a conference paper at ICPR 201

Computational methods in cardiovascular mechanics

The introduction of computational models in cardiovascular sciences has been progressively bringing new and unique tools for the investigation of the physiopathology. Together with the dramatic improvement of imaging and measuring devices on one side, and of computational architectures on the other one, mathematical and numerical models have provided a new, clearly noninvasive, approach for understanding not only basic mechanisms but also patient-specific conditions, and for supporting the design and the development of new therapeutic options. The terminology in silico is, nowadays, commonly accepted for indicating this new source of knowledge added to traditional in vitro and in vivo investigations. The advantages of in silico methodologies are basically the low cost in terms of infrastructures and facilities, the reduced invasiveness and, in general, the intrinsic predictive capabilities based on the use of mathematical models. The disadvantages are generally identified in the distance between the real cases and their virtual counterpart required by the conceptual modeling that can be detrimental for the reliability of numerical simulations.Comment: 54 pages, Book Chapte

Performance comparision of secure and insecure VoIP environments

This paper deals with techniques of measuring and assessment of the voice transmitted in IP networks in secure and insecure environment using different virtual testbeds and a real implementation based on OpenSer. They realised their real platform, in order to understand how the voice services in IP network are affected by using the secure IP environment. The real performance test was implemented between VSB-Technical University in Ostrava and University degli studi of Milan

SRtP 2.0 \u2014 The Evolution of the Safe Return to Port Concept

In 2010 IMO (International Maritime Organisation) introduced new rules in SOLAS with the aim of intrinsically increase the safety of passenger ships. This requirement is achieved by providing safe areas for passengers and essential services for allowing ship to Safely Return to Port (SRtP). The entry into force of these rules has changed the way to design passenger ships. In this respect big effort in the research has been done by industry to address design issues related to the impact on failure analysis of the complex interactions among systems. Today the research activity is working to bring operational matters in the design stage. This change of research focus was necessary because human factor and the way to operate the ship itself after a casualty on board may have a big impact in the design of the ship/systems. Also the management of the passengers after a casualty is becoming a major topic for safety. This paper presents the state of the art of Italian knowledge in the field of system engineering applied to passenger ship address to safety improvement and design reliability. An overview of present tools and methodologies will be offered together with future focuses in the research activity

Geographical heterogeneity of clinical and serological phenotypes of systemic sclerosis observed at tertiary referral centres. The experience of the Italian SIR-SPRING registry and review of the world literature

Introduction: Systemic sclerosis (SSc) is characterized by a complex etiopathogenesis encompassing both host genetic and environmental -infectious/toxic- factors responsible for altered fibrogenesis and diffuse microangiopathy. A wide spectrum of clinical phenotypes may be observed in patients' populations from different geographical areas. We investigated the prevalence of specific clinical and serological phenotypes in patients with definite SSc enrolled at tertiary referral centres in different Italian geographical macro-areas. The observed findings were compared with those reported in the world literature.Materials and methods: The clinical features of 1538 patients (161 M, 10.5%; mean age 59.8 +/- 26.9 yrs.; mean disease duration 8.9 +/- 7.7 yrs) with definite SSc recruited in 38 tertiary referral centres of the SPRING (Systemic sclerosis Progression INvestiGation Group) registry promoted by Italian Society of Rheumatology (SIR) were obtained and clustered according to Italian geographical macroareas.Results: Patients living in Southern Italy were characterized by more severe clinical and/or serological SSc phenotypes compared to those in Northern and Central Italy; namely, they show increased percentages of diffuse cutaneous SSc, digital ulcers, sicca syndrome, muscle involvement, arthritis, cardiopulmonary symptoms, interstitial lung involvement at HRCT, as well increased prevalence of serum anti-Scl70 autoantibodies. In the same SSc population immunusppressive drugs were frequently employed. The review of the literature underlined the geographical heterogeneity of SSc phenotypes, even if the observed findings are scarcely comparable due to the variability of methodological approaches.Conclusion: The phenotypical differences among SSc patients' subgroups from Italian macro-areas might be correlated to genetic/environmental co-factors, and possibly to a not equally distributed national network of information and healthcare facilities

DOPSIE: Deep-Order Proximity and Structural Information Embedding

Graph-embedding algorithms map a graph into a vector space with the aim of preserving its structure and its intrinsic properties. Unfortunately, many of them are not able to encode the neighborhood information of the nodes well, especially from a topological prospective. To address this limitation, we propose a novel graph-embedding method called Deep-Order Proximity and Structural Information Embedding (DOPSIE). It provides topology and depth information at the same time through the analysis of the graph structure. Topological information is provided through clustering coefficients (CCs), which is connected to other structural properties, such as transitivity, density, characteristic path length, and efficiency, useful for representation in the vector space. The combination of individual node properties and neighborhood information constitutes an optimal network representation. Our experimental results show that DOPSIE outperforms state-of-the-art embedding methodologies in different classification problems